No long-term effect of intracoelomic acoustic transmitter implantation on survival, growth, and body condition of a long-lived stenotherm in the wild
Bibliographic record
Abstract
A fundamental assumption of biotelemetry studies is that there are no adverse consequences from the surgical implantation or presence of the acoustic transmitter. In fisheries, most studies have evaluated this assumption over only short time periods (<2 years) in a laboratory setting. Here we compared the survival, growth, and body condition of populations of lake trout (Salvelinus namaycush) in three lakes containing tagged and untagged individuals over a 12-year period (2002–2013). We found no significant negative effects of acoustic telemetry tagging on the long-term survival of fish (estimates of combined annual survival ranged from 67% to 91%) and no negative effect of surgical implantation on growth or body condition for fish of either sex. Additionally, we found no significant effect of transmitter:fish mass ratio on fish survival, growth (with the exception of smaller-bodied fish in one lake), or condition. Our results indicate the use of transmitters weighing <1.25% (in water) of fish mass is a desirable criterion for larger-bodied adult lake trout. Our findings support the assumption that long-lived fish species tagged with acoustic transmitters via intracoelomic surgery survive, grow, and maintain body condition similar to untagged conspecifics over the long term in the wild.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".